Generally speaking, data patterns, and indicia such as bar code patterns, data matrix patterns, Optical Character Recognition (OCR) fonts, text characters, graphic images, logos and other one dimensional (1D) and two dimensional (2D) patterns of geometric and graphic data (referred to herein as “patterns”) are useful in a wide variety of applications. Some printers and printing evaluating processes may be specialized for efficient printing of the data patterns on labels or other graphic media. For example, bar code printers may thus be widely deployed in various supply chain and identification applications.
Some bar codes, data patterns and other symbols comprise information of significant relevance, importance, or substance in relation to an operation, endeavor, or enterprise (“operation”). Some of the significant information may be mission-critical to an operation. The success of the operation may depend, at least in part, on the mission-critical information. Accurate presentation, transactional reliability, and security thus become significant factors in relation to mission-critical information. Such data may also have a high time value, low duration of fresh relevance, and related heightened levels of urgency, which may make timely handling or responsiveness appropriate based on the accurate presentation.
In view of their significance, quality related verification is a significant feature of various printing evaluating processes and printing evaluating systems are thus associated with the production of mission-critical printed media. The printed data are verified using scanning and validation processes to compare an output instance of an image with a stored digital reference or programmed original instance of the image. An acceptable correlation may be determined based on the comparison. For example, alphanumeric, pictographic, or character based, and other text related data may be verified using an OCR process in relation to clarity, legibility, readability, and correct conformance to the reference or original.
Barcodes and other data patterns may be verified based on a scanning process. For example, a brief, simple scan may be performed to verify that a barcode pattern, QR code pattern or the like is actually scannable, and may thus be read, decoded, and stored. Additionally or alternatively, the data patterns may be subject to scanning to ascertain their compliance with a programmed quality specification, and/or to quality standards promulgated by the American National Standards Institute (ANSI), International Electrotechnical Commission (IEC) International Organization for Standardization (ISO), and other authorities.
For example, 1D Universal Product Code (UPC) and 2D matrix data patterns may be specified to comply with quality specifications set forth in the ‘ANSI/UCC5’ standard. Linear (1D) barcode patterns may be specified to comply with quality specifications set forth in the ‘ISO/IEC 12516’ standard. Quick Response (QR), Han Xin, and other 2D data patterns may be specified to comply with quality specifications set forth in the ‘ISO/IEC 15415’ standard.
These verification techniques however may be associated with nontrivial costs in relation to operator time, attention, and diversion from more productive and/or profitable activity. Moreover, access to reference instances corresponding to printed output products reflective of intended, original, programmed, stored, modeled, and/or otherwise “correct” printed product outcomes, may be lacking, unavailable, stale or corrupt.
Separate technologies and independent applications may be used to fully verify the correctness of the data. These however may tend to add complexity, cost, and the possibility of introducing inaccuracy. An OCR algorithm may be used in an effort to compute an estimate or essentially “guess” at the correctness of a printout without reference to actual input or other reference data on which the printout is ostensibly based.
For example, the ‘Arabic’ numeral ‘4’ may be modeled for printing a corresponding feature with an open upper portion. However, OCR may read a ‘4’ character as “correct,” which has the upper portion closed by the vertex of an acute angle. The OCR may thus fail to ascertain actual compliance of an output print product to a reference input.
To mitigate the effects of latency and costs associated with visual examination of print products, inspections may be limited to “spot checks.” However, such spot checks are typically performed only over portions of an entire print product. The print product portions are typically significantly smaller than the entire print product. For example, while a print product may comprise a total of 100, 1000, or 10,000 labels a corresponding spot check performed over five percent (5%) of the total product samples only five (5), 50 or 500 of the product, respectively. These spot checks essentially thus overlook 95, 950, or 9,500 of the labels, respectively. Such visual inspections may miss some quality deficient labels and may thus be error-prone, in least over the major portions of the print products that remain unexamined. Thus, the actual correctness of any printout, in its entirety, may remain effectively indeterminate and best on a statistically inferred quality level. Imperfect individual products may escape notice.
Some contemporary applications however may rely however, at least in part, on verifying the accuracy of the printed products. For example, accuracy in the labeling of prescription drugs may comprise a serious quality specification for printing evaluating processes undertaken by pharmacies and other health care endeavors. Lifesaving drugs, powerful narcotics, radioactive pharmaceuticals, and therapeutic substances and solutions may be dangerous if dosed or otherwise used improperly or incorrectly provide a clear and high example of the importance of accurate labeling.
In these respects, verifying the accurate printing of correct labels for medicine may thus be considered mission-critical to pharmacies and in other health care scenarios. Verification based the typical OCR and visual examination approaches may be insufficient in such mission-critical printing applications.
Therefore, it would therefore be useful to verify printed media products of mission-critical printing processes to confirm that information presented by output images correspond accurately to original instances or input digital images, on which the printing is based. It would also be useful to verify the printed media products without necessarily implicating, or resorting to either OCR based confirmation of text related images or for printed data patterns, to grading related to standards, specifications, and/or simplistic scannability checks. Further, it would be useful to verify the printed media products automatically with a high degree of accuracy and testing throughput speed, which obviates “spot checking” of mere sampled portions of a total printing product output, yet adds no significant latency or demands on an operator attention.